# Multimodal Grounding Failures

The general class of outputs making claims the supplied non-text evidence does not support.

**Rests on:** Object Hallucination

**Leads to:** Cross-Modal Drift

*LLMs and Generative AI · Hallucination Mitigation*


## Why learn this

You are learning this because it is the most direct way a multimodal AI system — one that works with images, audio, or video alongside text — can fail. Before you can fix hallucination, you need to see exactly where the model's claims stop matching the evidence you gave it. This concept unlocks the rest of hallucination mitigation: why grounding beats instruction, why requiring citations makes fabrication detectable, why structured output constraints kill whole classes of invented format, and why teaching a system to abstain is often more valuable than forcing an answer. It also opens the door to the neighbouring topics in your library: Object Hallucination (the most studied form, where the model names things that aren't in the image) and Cross-Modal Drift (where the output sounds right for the general world, but not for this specific input). Without understanding grounding failures, every way you have to measure success later on will be measuring the wrong thing.


## The intuition

Imagine you are a detective looking at a photograph of a crime scene. The photo shows a kitchen table with a knife, a glass, and a plate. You write your report: 'The suspect held a revolver at the table.' There is no revolver in the photo. You have just produced a grounding failure — your words claim something the visual evidence does not support. A multimodal AI system (one that takes in both text and an image, or text and audio) does exactly this when it describes a picture or answers a question about it. Grounding means: every claim in the output should be traceable to something actually present in the non-text input. When the model's language is fluent and grammatically perfect, it is dangerously easy to miss that the content is about a thing that isn't there. Fluency and grammaticality are completely uncorrelated with grounding — a sentence can be polished and confident and still be about a ghost. The generated output itself is an evaluation surface: the text the model produces is not just an answer, it is also a place where you can look for claims and check them against the evidence. But you can't check automatically unless you build the system to make the check possible.


## An analogy

Think of a tour guide showing a group around a museum. The guide is extremely articulate and knows a lot about art history. But imagine the guide describing a painting of a bowl of fruit and saying, 'Here we see the artist's famous depiction of a violin and a candle.' The actual painting shows fruit, no violin, no candle. The guide's words are fluent, confident, even beautiful — but they are not grounded in the painting. The group trusts the guide because the guide sounds so sure. Now, why does the guide do this? Because the guide has been trained on thousands of descriptions of paintings, and in many of those, violin-and-candle still lifes appear. The guide is letting past training habits override the current visual evidence. In AI terms, we call that a language prior. The model has seen so many captions that mention violins in the context of still lifes that the word 'violin' is highly probable, and the model says it even though this particular image shows none. The tour guide analogy breaks down in one important way: a human guide can be corrected with a quick 'Actually, look again,' and can learn from the mistake. An AI model does not learn from a single correction — that would require retraining on new data, which is expensive and slow. This is why grounding failures cannot be fixed by just telling the model 'don't hallucinate' at the moment it answers. You have to change the system around the model: the evidence you give it, the constraints you impose, or the way you evaluate the output. The generated output is an evaluation surface — it's the place where you look for the failure, not inside the model's hidden weights — you can't open the model's head, but you can examine its words.


## Definition

A multimodal grounding failure is any output from an AI system that makes a claim about the non-text input (image, audio, video) that is not actually supported by that input — the model says something is present, absent, or happening that the evidence does not confirm.


## How it connects

This concept is the umbrella under which two neighbouring topics in your library sit: Object Hallucination (naming objects that are not in the image) and Cross-Modal Drift (producing output that is plausible for the general training distribution but not for this specific input). Object hallucination is the most studied and most direct form — you give the model a picture of a cat, and it says 'dog.' Cross-Modal Drift is subtler: the description could fit many images, not just the one in front of it. Both are grounding failures. You already have notes on Large Language Models (systems trained to predict text) and Retrieval-Augmented Generation (adding outside information to answers) in your library — this concept builds on those because grounding failures in multimodal systems are often the same failure as in pure text, but with the additional twist that the evidence is non-textual and harder to automatically verify. Understanding this concept is the first step toward hallucination mitigation's core principle: grounding beats instruction. You cannot just tell the model 'don't invent things' — you have to give it the evidence and make the output checkable. The learner has mastered nothing yet, so this page stands alone for now, but it will connect to your future notes on evaluation metrics and guardrails.


## The mathematics

This concept does not require any mathematical formulas — it is about the relationship between an output and its evidence, which is conceptual, not numerical. However, the underlying machinery of the model that produces the output does use mathematics, and understanding a little of it helps you see why grounding failures happen. The branch of mathematics involved is probability and statistics, because the model is essentially assigning probabilities to different possible words. The key idea is a conditional probability: the probability of a word given the image. The model calculates, for each possible next word, how likely it is based on both the language patterns it learned during training and the visual features it extracted from the image. In an ideal world, the visual evidence would dominate when there is a conflict. But in practice, the language prior — how common the word is in general text — can overpower the visual signal. This is not a complex equation you need to calculate by hand; it's a weighting between two influences. Think of it as a balance scale: on one side, the visual evidence says 'no violin,' on the other side, the language model's training says 'violin is a very common word in art descriptions.' If the scale tips toward language, the model outputs 'violin' — a grounding failure. There is no formula to compute that balance directly, but understanding that it is a balance between two sources of information is the core. The measure of whether a grounding failure exists is not a number you compute, but a comparison: check if each claim in the output is supported by the evidence. That comparison is binary — supported or not — but you can count how many claims fail, giving a rate.

Grounding failure rate = ( number of unsupported claims ) / ( total number of claims ) × 100%


## A worked example

```
Consider a simple example. You give a multimodal model a picture of a beach with no people. The model is asked to describe the image.

Step 1: Claim 1: 'The beach is sandy.'   # This is supported by the image — the beach clearly shows sand.
Step 2: Claim 2: 'A child is building a sandcastle.'   # This is not supported — the image contains no child and no sandcastle.
Step 3: Claim 3: 'The sky is blue.'   # This is supported — the sky is indeed blue in the image.

Result: Out of 3 claims, 2 are grounded, 1 is not. The grounding failure rate is 1/3, or about 33%. Each claim is checked individually against the evidence.
```


## From scratch, in Python

```python
# We'll build a tiny evaluator that checks claims against a simple 'image description'.
# We don't have a real image, so we'll use a list of known objects (the 'visual evidence').

# Step 1: The Python building block we need is a list. A list holds items in order.
# Here's a list of objects that ARE in the image:
image_objects = ["beach", "sand", "sea", "sky", "sun"]

# Step 2: The model's output (the claims we want to check).
# We'll store them as a list of sentences.
model_claims = [
    "The beach is sandy.",
    "A child is building a sandcastle.",
    "The sky is blue."
]

# Step 3: We need to check each claim for the presence of one of the known objects.
# We'll define a function that takes a claim and the list of objects, and returns True/False.
# A function is a reusable block of code - we give it inputs, it gives back an output.

def is_claim_grounded(claim, objects):
    # Convert the claim to lowercase so 'The' matches 'the'.
    claim_lower = claim.lower()
    # Loop over each object in the list.
    for obj in objects:
        # If the object's name appears in the claim, we consider it grounded.
        if obj in claim_lower:
            return True
    # If we finish the loop without finding any object, the claim is not grounded.
    return False

# Step 4: Now we evaluate each claim.
# We'll keep a count of how many are grounded and how many are not.
grounded_count = 0
unsupported_count = 0

for claim in model_claims:
    if is_claim_grounded(claim, image_objects):
        grounded_count = grounded_count + 1
        print("Grounded:", claim)
    else:
        unsupported_count = unsupported_count + 1
        print("UNSUPPORTED:", claim)

# Step 5: Compute the grounding failure rate.
total_claims = len(model_claims)  # len() gives the number of items in a list
failure_rate = (unsupported_count / total_claims) * 100

print("Grounding failure rate: {:.1f}%".format(failure_rate))
```


## What the code does

The code defines a list of objects that are in the image. Then it defines a function `is_claim_grounded` that checks if any object name appears in the claim. It loops through each claim, prints whether it is grounded or not, and counts. Finally, it computes the failure rate. This is a toy version of what real evaluation systems do — they extract claims from the output and check each one against the image (using object detectors or vision models). The key point is that the check is per-claim, not a single global score.


## Common misconceptions

**Fluent output means the model is correct** — If the model says something in perfect English, it's trustworthy.

Fluency and grammaticality are completely uncorrelated with grounding. A sentence can be polished, confident, and entirely unsupported by the evidence. The model is trained to produce fluent text, not necessarily truthful text. In fact: Always check each claim against the evidence independently of how fluent the output sounds. Grounding is about content, not style.

**The model stores the image like a photo** — The model has the image stored in its memory and can look at it pixel by pixel.

The model processes the image into a compressed set of features, and then generates text based on those features plus its language knowledge. It doesn't 'see' the image the way a human does; it sees a numerical summary that may lose detail. In fact: The model uses a visual representation, but that representation is lossy and can be overridden by language priors.

**One word error is a minor issue** — If the model says 'child' instead of 'adult', it's just a small slip, not a serious failure.

In many applications, a single wrong object can be catastrophic (e.g., medical diagnosis, autonomous driving). The failure is not about the size of the word, but about the fact that the system is making unsupported claims at all. In fact: Treat any grounding failure as a systemic issue, not a minor typo. The root cause is the same — the model is not anchored to the evidence.

**Better prompting fixes hallucinations** — If I just tell the model 'please be accurate' or 'don't make things up', it will stop hallucinating.

Instruction can reduce but not eliminate grounding failures. The model's behavior is driven by its training and the statistical likelihoods, not by admonitions. Grounding beats instruction — you need to give the model the evidence and check the output, not just ask it to be good. In fact: Use retrieval, citations, and verification mechanisms to ensure claims are supported.

**Grounding failure is the same as a grammar error** — If the output is grammatically correct, then it is grounded.

Grammar is about syntax, grounding is about semantics and truth. A grammatically perfect sentence can be completely fabricated. In fact: Evaluate content against the evidence, not just the form of the sentence.


## In short

A multimodal grounding failure is when the model's output claims something that the provided image, audio, or video does not support. It is the most direct error mode in multimodal AI. Key points: (1) Fluency does not equal grounding — always check the content. (2) The generated output itself is your evaluation surface — inspect its claims. (3) The causes are the model's training priors overpowering the visual evidence. (4) To mitigate, you must ground the output via evidence provision, citation, detection, or abstention. Remember the tour guide: fluent, confident, but wrong. Your job as a builder is to catch it.


## Where this is used

Multimodal grounding failures are a central concern in building AI systems that use images, audio, or video alongside text. They appear in:

- **Large Language Models with vision (multimodal LLMs)**: When you ask a model like GPT-4V to describe an image, it can produce fluent descriptions that mention objects that are not there. For example, if you show it a picture of a park without dogs, it might say 'a dog is playing' because dogs appear in many park images in its training. The mechanism is that the language model generates words that are likely given both the visual features and the language context, and sometimes the language prior dominates. A developer building a system that relies on such descriptions (e.g., a medical image report generator) cannot trust the output blindly – they need to verify each claim against the image. In practice, they might use a separate object detector (a vision model) to independently confirm what is present, and then filter out any claims that the detector does not support.
- **Small models running on devices** (e.g., a smartphone app that answers questions about photos you take): These models are often smaller and have less visual capacity, so they are more prone to grounding failures. A developer might use a lightweight object detector on the device to constrain the model's vocabulary to only the detected objects, or they might rely on a post-hoc check that sends the image and the claim to a verification model. The mechanism is the same: the claim must be checked against the actual visual evidence.
- **Retrieval-Augmented Generation (RAG)**: This is not only about text – you can retrieve relevant images or video segments and feed them to the model. A grounding failure happens when the model's answer refers to a detail that is not in any retrieved image. The mechanism is that the model sees the retrieved image but still relies on its internal knowledge. To mitigate, developers often require the model to cite which image supports each claim, and then they validate the citation by checking that the cited image actually contains the referenced object. This makes fabrication detectable.
- **Agentic systems** that plan, call tools, and act in loops: An agent might take a photo as input, plan an action (e.g., 'the room is messy, so I will pick up objects'), but if the image does not actually contain those objects, the agent's action will be wrong. The mechanism is that the agent's planning module uses the multimodal input to decide actions; a grounding failure leads to incorrect actions. A developer might add a strict rule: before taking an action, verify that the object is present using a separate vision model. If not, abstain from acting or ask for clarification.
- **Everyday applied machine learning** – recommendation systems (based on product images), vision systems (self-driving cars – a car should not say 'pedestrian ahead' if no pedestrian is there), fraud detection (verifying identity documents – the model must not claim features that are not on the ID), speech (transcribing a video and then describing what is on screen). In all these, grounding failures can have serious consequences – a self-driving car that hallucinates a pedestrian could brake unnecessarily, or worse, fail to brake for a real one because it was not grounded.

**What a developer actually writes**: They write evaluation scripts that take the model's output, extract claims (either using a language model or simple heuristics), and check each claim against a reliable source of truth (e.g., a human annotation, a detection model, or a database of known facts). They also write guardrails that block outputs that fail to ground. For example, a Python script that calls an object detector, gets the list of objects, and then filters the model's text to remove any sentence that refers to an object not in the list. If the model says 'the child is playing,' but the detector only found 'beach', the sentence is rejected and the model might be asked to regenerate, or the system abstains.

**What breaks when done badly**: If you do not check grounding, the system can confidently produce fabricated information. In medical imaging, that could lead to a false diagnosis. In a self-driving car, it could lead to an accident. In a customer service bot that analyzes screenshots, it could give wrong advice. The cost of a single grounding failure can be huge, which is why this concept is critical.


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Read this on the web: https://basavapurushottam.com/learn-ai/multimodal-grounding-failures
